class_docstr: Cifar10 dataset with CNN network model.
code: def _get_layers(self, nn, lidx, num_incoming_filters=None):
      # Inputs:
      #   nn - neural_network object
      #   lidx - layer index
      #   num_incoming_filters - number of filters from parents (after concatenation)
      layerStr = nn.layer_labels[lidx]
      strideVal = nn.strides[lidx]
      poolSizeVal = 2
      stridePoolVal = 2
      num_filters = nn.num_units_in_each_layer[lidx]
      if num_filters == None:
          num_filters = 1
      if layerStr == "relu":
          return lambda x: self._relu_layer(x)
      elif layerStr == "conv3":
          return lambda x: self._conv_layer(
              x, 3, num_filters, strideVal
          )
      elif layerStr == "conv5":
          return lambda x: self._conv_layer(
              x, 5, num_filters, strideVal
          )
      elif layerStr == "conv7":
          return lambda x: self._conv_layer(
              x, 7, num_filters, strideVal
          )
      elif layerStr == "conv9":
          return lambda x: self._conv_layer(
              x, 9, num_filters, strideVal
          )
      elif layerStr == "res3":
          return lambda x: self._residual_layer(
              x, 3, num_incoming_filters, num_filters, strideVal
          )
      elif layerStr == "res5":
          return lambda x: self._residual_layer(
              x, 5, num_incoming_filters, num_filters, strideVal
          )
      elif layerStr == "res7":
          return lambda x: self._residual_layer(
              x, 7, num_incoming_filters, num_filters, strideVal
          )
      elif layerStr == "res9":
          return lambda x: self._residual_layer(
              x, 9, num_incoming_filters, num_filters, strideVal
          )
      elif layerStr == "avg-pool":
          return lambda x: self._avg_pool_layer(
              x, poolSizeVal, stridePoolVal
          )
      elif layerStr == "max-pool":
          return lambda x: self._max_pool_layer(
              x, poolSizeVal, stridePoolVal
          )
      elif layerStr == "fc":
          return lambda x: self._fully_connected_layer(x, num_filters)
      elif layerStr == "softmax":
          num_filters = self.num_classes
          return lambda x: self._softmax(x, num_filters)
  
file_path: dragonfly/examples/nas/cg/cifar/cifar10_model.py
imports: from __future__ import division
  from __future__ import print_function
  import tensorflow
  from cifar10_model_base import ConvNet
label_desc: Use image node_id 2 for calling the ConvNetCifar10 obj's underlying member method code with example usage: obj._get_layers(nn, lidx, num_incoming_filters) and returns: function, function, function, function, function, function, function, function, function, function, function, function, function